Abstract
There are thousands of distinct disease entities and concepts, each of which are known by different and sometimes contradictory names. The Monarch Initiative aims to integrate genotype, phenotype, and disease knowledge from a large variety of sources in support of improved diagnostics and mechanism discovery through various algorithms and tools. However, the lack of a unified system for managing disease entities poses a major challenge for both machines and humans to predict causes and treatments for disease. The multitude of disease resources have not been well coordinated nor computationally integrated. Furthermore, the classification of phenotypes and their association with diseases is another source of disagreement across sources. The Human Phenotype Ontology has helped to standardize phenotypic features across knowledge sources, but there was no equivalent computationally-harmonized disease ontology. To address these problems, a community of disease resources worked together to create the Mondo Disease Ontology as an open, community-driven ontology that integrates key medical and biomedical terminologies and is iteratively and regularly updated via manual curation and through synchronization with external sources using a Bayesian algorithm. Mondo supports disease data integration to improve diagnosis, treatment, and translational research. It records the sources of all data and is continually updated, making it suitable for research and clinical applications that require up-to-date disease knowledge.
Evidence before this study Many disease terminologies currently exist, but there is not a definitive standard for encoding diseases while addressing requirements for information exchange. Existing sources of disease definitions include the National Cancer Institute Thesaurus (NCIt), the Online Mendelian Inheritance in Man (OMIM), Orphanet, SNOMED CT, Disease Ontology (DO), ICD-10, MedGen, and numerous others. Each of these is designed for a particular purpose, and as such has different strengths. However, these standards only partially overlap and often conflict in the classification or mapping approach, making it difficult to align them with each other and/or with other knowledge sources. This need to integrate information has resulted in a proliferation of mappings between disease entries in different resources; these mappings lack completeness, accuracy, and precision, and are often inconsistent between resources.
Added value of this study In order to computationally leverage the available knowledge sources for diagnostics and to reveal underlying mechanisms of diseases, we need to understand which terms are meaningfully equivalent across different resources. This will allow integration of associated information, such as treatments, genetics, phenotypes, etc. We therefore created the Mondo Disease Ontology to provide a logic-based structure for unifying multiple disease resources.
Implications of all the available evidence Mondo can be leveraged by researchers and clinicians for disease annotations and data integration to aid in clinical diagnosis, treatment and advancement of human health care. Mondo is a freely available, open terminology that contains over 20,000 disease classes. Mondo is iteratively developed with contributions from the intended community and is under continuous revision, with future plans to further revise the top-level classes. Recently, efforts to classify rare diseases have centered on retrieving terms from various sources to provide a unified resource. Mondo can be explored using any of a variety of ontology browsers such as the Ontology Lookup Service (OLS) (ebi.ac.uk/ols/ontologies/mondo), and the ontology files and current releases are available on GitHub (github.com/monarch-initiative/mondo).
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
Mondo is generously supported by the NIH National Human Genome Research Institute Phenomics First Resource, NIH-NHGRI # 1 RM1 HG010860-01, a Center of Excellence in Genomic Science; and an NIH Office of the Director Grant #5R24OD011883 for the Monarch Initiative. Additional support for this research/work was supported in part by the National Center for Biotechnology Information of the National Library of Medicine (NLM), National Institutes of Health.
Author Declarations
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Footnotes
We updated the author list to include one additional author and made minor revisions to the manuscript.
Data Availability
All data produced are available online at https://github.com/monarch-initiative/mondo.
